Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use standards. This is because 3D vessel structures are highly complicated a...
3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a...
Detailed anatomical labeling of bronchial trees extracted from CT images can be used as fine-grained maps for intra-operative navigation. To cater to the sparse distribution of airway voxels and large class imbalance in 3D image space, a graph-neural...
In vivo application of intravascular photoacoustic (IVPA) imaging for coronary arteries is hampered by motion artifacts associated with the cardiac cycle. Gating is a common strategy to mitigate motion artifacts. However, a large amount of diagnostic...
The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at le...
Pre-processing is widely applied in medical image analysis to remove the interference information. However, the existing pre-processing solutions mainly encounter two problems: (i) it is heavily relied on the assistance of clinical experts, making it...
Proceedings of the National Academy of Sciences of the United States of America
Dec 28, 2022
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional...
We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an...
Environmental monitoring and assessment
Dec 28, 2022
Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied...
Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally ...
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